Iraqi Journal of Information and communication technology

With the rapid growth of cloud computing and the expansion of mobile phone users in recent years ,mobile cloud computing has attracted wide attention. Wireless computing networks are the basis of data sharing between mobile devices and cloud services in the mobile cloud. Since air is the communication medium, it must be properly protected; otherwise, it will be subject to a variety of security threats, for example, attacks from middle-man, identity tracking, etc. Furthermore, mobile devices are limited in storage, resources, and computing powers. Hence, designing an efficient and secure balance of authentication schemes is extremely important. First of all, a multi-factor authentication scheme based on biometric (fingerprint information), hash function, and fuzzy vault algorithm is presented in that paper. Secondly, the Validation and Analysis tool of AVISPA Security was approved. Finally, the proposed scheme’s security is compared to that of other related schemes

Cryptography is a scientific approach to transmitting information securely. While quantum cryptog- raphy relies on the physical rules to encrypt information. When the quantum computer debuted, the traditional cryptographic approach became less efficient. The quantum approach is frequently used to distribute keys across communication entities, which is known as quantum key distribution (QKD). The primary paradigm of QKD was initiated for a depolarization channel. Quantum key distribution is a secure method for exchanging keys across communication entities. The first QKD protocol, BB84, was introduced in 1984. The primary paradigm of QKD was initiated for a depolarizing channel. In traditional cryptography, secret keys are sent over a secure communication channel. The problem arises when both parties are unable to establish a secure channel for the first key exchange, so all their correspondence may be intercepted and decrypted by a third party that obtained the key during the initial key exchange. This paper demonstrates the ability to use MATLAB to simulate QKD. It has been found that the security of the QKD protocol depends on a variety of factors. It provides security tests, recommendations, and guidance on the lower and higher bounds of significant parameters such as the depolarizing channel parameter, number of input photons, and Eve’s attack level for a target secret-key rate, as well as insightful outputs such as QBER, secret-key rates, the mutual information between Alice and Bob or Alice and Eve, and the expected number of bits to be leaked.

Malicious software known as ransomware encrypts or blocks access to files on a computer to extract a ransom from the user. Ransomware works in a variety of ways, from encrypting all the infected computer’s data to merely locking the desktop. Restoring functionality or decrypting files is only possible once you pay the ransom ware software with no guarantee to restore data. Unfortunately, ransomware that can identify its environment might elude dynamic analysis’s valuable job. To build an effective and powerful detecting approach, this paper introduces a new technique based on static analysis to detect and classify ransomware using five machine learning techniques .First, n-gram features were extracted from samples and CF-NCF values were computed. Then the gain ratio approach was utilized to choose the most essential characteristic. Finally, the vectors of n-grams have been sent to machine-learning techniques to classify ransomware. To evaluate the proposed method, ten evaluation metrics have been used. Using real datasets, the proposed approach shows its ability to reliably identify between good ware and ransomware files successfully with an accuracy of classification equal to 98.33%

Video anomaly detection in smart cities is a critical errand in computer vision that plays an imperative role in intelligent surveillance and public security but is challenging due to its different, complex, and rare events in real-time surveillance situations. Different deep learning models utilize a critical amount of training data without generalization capabilities and with high time complexity. To overcome these problems, an algorithm for reducing the size of the map of the extracted features has been suggested, and this was done by combining the features of every 15 video frames to generate the new feature vectors that will be fed into our classifier model. The values of the new feature vectors represent the summation of the values of the original feature vectors obtained from Resnet50.Finally, the new feature vectors are fed into our classifier model to detect the abnormality. A comprehensive test on a variety of anomaly detection benchmark datasets has been conducted to verify the proposed frame work’s functionality in complex surveillance scenarios. The numerical results were carried out on the UCF-Crime dataset, with the proposed approach achieving Area Under Curve (AUC) scores of 93.61% on the database’s test se